Today the costs of a failure in operation of huge
industrial complexes are very high. Traditional approaches
for defect detection in automation systems engineering in
principle work, but generally don´t take into account the
semantic heterogeneity of tools and data models which are
used within the engineering of industrial automation systems.
Thus, some defects can remain undetected. Also, such systems
have to be implemented anew for each concrete case. In this
paper we present our ongoing and planned research aimed
to improve the defect detection processes. Our approach is
based on using explicit knowledge about industrial system
stored in a set of ontologies which integrate information
from different heterogeneous data sources and present it in
machine-understandable form. Another important part of the
approach is rules describing system´s logic. Such rules can,
through the use of integrated engineering knowledge stored in
ontologies, detect faults which otherwise are hard to identify
using traditional methods. Major expected results are the more
efficient and effective defect detection and the potential reuse
of the created ontologies in other projects.